A novel approach for enhancement of geometric and contrast resolution properties of low contrast images

Singh, Koushlendra Kumar; Bajpai, Manish Kumar; Pandey, Rajesh K.

doi:10.1109/jas.2017.7510670

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…However, this approach allows for the detection of high frequency changes, such as only those on the edges (boundaries) of objects. The improvement of image quality can also be based on the algorithm for improving the contrast, dedicated for low-contrast images [63]. This algorithm allows better visibility of details, but also is based on edge detection.…”

Section: Methodsmentioning

confidence: 99%

Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

Sekrecka

Kędzierski

2018

Sensors

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Commonly used image fusion techniques generally produce good results for images obtained from the same sensor, with a standard ratio of spatial resolution (1:4). However, an atypical high ratio of resolution reduces the effectiveness of fusion methods resulting in a decrease in the spectral or spatial quality of the sharpened image. An important issue is the development of a method that allows for maintaining simultaneous high spatial and spectral quality. The authors propose to strengthen the pan-sharpening methods through prior modification of the panchromatic image. Local statistics of the differences between the original panchromatic image and the intensity of the multispectral image are used to detect spatial details. The Euler’s number and the distance of each pixel from the nearest pixel classified as a spatial detail determine the weight of the information collected from each integrated image. The research was carried out for several pan-sharpening methods and for data sets with different levels of spectral matching. The proposed solution allows for a greater improvement in the quality of spectral fusion, while being able to identify the same spatial details for most pan-sharpening methods and is mainly dedicated to Intensity-Hue-Saturation based methods for which the following improvements in spectral quality were achieved: about 30% for the urbanized area and about 15% for the non-urbanized area.

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Section: Methodsmentioning

confidence: 99%

Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

Sekrecka

Kędzierski

2018

Sensors

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“…According to trial results, this paper [7] proposed framework may produce vehicles and the scenery that surrounds them with variations and varied levels of detail. The GAN with reconstruction and style transfer losses and no encoder has been shown to be a practical model for producing maps in this paper [8]. The generator is trained as a normalizing flow (RealNVP) model and has a conditional Generative Adversarial Network (GAN) that compresses the images to a learned embedding.…”

Section: 𝐼𝑥 = 𝐷(𝐼𝑦)mentioning

confidence: 99%

Implementation of Super Resolution Techniques in Geospatial Satellite Imagery

Baraskar,

Raman,

Panchal

2023

IJRITCC

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The potential for more precise land cover classifications and pattern analysis is provided by technological advancements and the growing accessibility of high-resolution satellite images, which might significantly improve the detection and quantification of land cover change for conservation. A group of methods known as "super-resolution imaging" use generative modelling to increase the resolution of an imaging system. Super-Resolution Imaging, which falls under the category of sophisticated computer vision and image processing, has a variety of practical uses, including astronomical imaging, surveillance and security, medical imaging, and satellite imaging. As computer vision is where deep learning algorithms for super-resolution first appeared, they were mostly created on RGB images in 8-bit colour depth, where the sensor and camera are separated by a few meters. But no evaluation of these methods has been done.

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“…A total number of ‘seeds’ are 1024. 5: For every block, fix the mid‐frequency range index, where the watermark signal has to be added. In our case, we consider the indices [5–7, 12–14, 19–21, 26–28, 33–35]. 6: The size of DSSS is 15.…”

Section: Proposed Techniquementioning

confidence: 99%

“…Here, we explore the concept of a fractional differentiator (FD) for medical image watermarking application. The FD has shown an increasing useful interest and has been triggered as a prominent branch of mathematical analysis of signals [19] and it has been employed in many signal and image processing applications such as image enhancement [20], image denoising [21] etc . It has been demonstrated by scientific findings that fractional order approach is best suited to many natural phenomena due to its non‐linear behaviour.…”

Section: Introductionmentioning

confidence: 99%

FD‐based detector for medical image watermarking

Kumar

Jha

2019

IET image process

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In this study, the authors propose a novel technique for medical image watermark detection using the concept of the fractional differentiator (FD). The feature of FD as a non‐linear high‐pass filter helps in watermark detection. In the region of non‐interest, the watermark image has been added in a mid‐band frequency range of the discrete cosine transform coefficients of 8×8 different blocks by generating direct spread spectrum sequence. Their scheme produces noise‐free watermarked medical images. Furthermore, they derive the test statistics of the proposed detector, which is characterised by the fractional order q. The average errors in pixels (PEs), peak‐signal‐to‐noise ratio (PSNR), structural similarity index (SSIM) and cross‐correlation coefficient (CC) have been used to quantify the capability of the proposed technique over some state‐of‐the‐art techniques. The proposed technique shows that at a particular value of fractional order q, there is a significant reduction in average PEs. It causes an increment in PSNR, SSIM and CC. The proposed technique is tested on a large number of medical images and it is found that their proposed technique works better or comparable with other state‐of‐the‐art techniques.

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A novel approach for enhancement of geometric and contrast resolution properties of low contrast images

Cited by 14 publications

References 30 publications

Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

Implementation of Super Resolution Techniques in Geospatial Satellite Imagery

FD‐based detector for medical image watermarking

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